Advanced Lane Finding¶
In [82]:
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from moviepy.editor import VideoFileClip
from collections import deque
import os
import glob
from os import listdir
from os.path import isfile, join
%matplotlib inline
Camera Calibration.¶
Use /camera_cal to adjust for distortion. Orig on left, corrected on right. Step one: Plot cornders and correct to 9.6 as per instructions. Quality control by visual inspection
In [83]:
#Step 1: verify corners are well behaved
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9, 0:6].T.reshape(-1,2)
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
images = glob.glob('camera_cal/*.jpg')
for idx, fname in enumerate(images):
print ('processing:', fname)
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6), None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
cv2.drawChessboardCorners(img, (9,6), corners, ret)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,4))
ax1.imshow(cv2.cvtColor(mpimg.imread(fname), cv2.COLOR_BGR2RGB))
ax1.set_title('Original Image', fontsize=18)
ax2.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
ax2.set_title('With Corners', fontsize=18)
In [84]:
# Remove distortion from images
def undistort(image, show=True, read = True):
if read:
img = cv2.imread(image)
img_size = (img.shape[1], img.shape[0])
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
undist = cv2.undistort(img, mtx, dist, None, mtx)
if show:
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(9,6))
ax1.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
ax1.set_title('Original Image', fontsize=18)
ax2.imshow(cv2.cvtColor(undist, cv2.COLOR_BGR2RGB))
ax2.set_title('Undistorted Image', fontsize=18)
else:
return undist
In [85]:
images = glob.glob('test_images/*.jpg')
for image in images:
print ('processing:', image)
undistort(image)
In [96]:
# Perform perspective transform
class Constant:
def __init__(self):
self.TOP_LEFT=[490,480]
self.TOP_RIGHT=[810,480]
self.BOTTOM_RIGHT=[1250,700]
self.BOTTOM_LEFT=[40,720]
self.src = np.float32([TOP_LEFT,TOP_RIGHT,BOTTOM_RIGHT,BOTTOM_LEFT])
self.dst = np.float32([[0, 0], [1280, 0], [1250, 720],[40, 720]])
def birds_eye(img, display=True, read = True):
if read:
undist = undistort(img, show = False)
else:
undist = undistort(img, show = False, read=False)
const = Constant()
img_size = (undist.shape[1], undist.shape[0])
#print (undist.shape)
src = np.float32([const.TOP_LEFT,const.TOP_RIGHT,const.BOTTOM_RIGHT,const.BOTTOM_LEFT])
dst = np.float32([[0, 0], [1280, 0], [1250, 720],[40, 720]])
M = cv2.getPerspectiveTransform(src, dst)
warped = cv2.warpPerspective(undist, M, img_size)
if display:
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(9, 6))
f.tight_layout()
cv2.line(undist,(490,480),(810,480),(255,0,0),3)
cv2.line(undist,(1250,720),(810,480),(255,0,0),3)
cv2.line(undist,(40,720),(1250,720),(255,0,0),3)
cv2.line(undist,(490,480),(40,720),(255,0,0),3)
ax1.imshow(cv2.cvtColor(undist, cv2.COLOR_BGR2RGB))
ax1.set_title('Undistorted Image', fontsize=20)
ax2.imshow(cv2.cvtColor(warped, cv2.COLOR_BGR2RGB))
ax2.set_title('Undistorted and Warped Image', fontsize=20)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
else:
return warped, M
In [97]:
for image in glob.glob('test_images/*.jpg'):
print (image)
birds_eye(image)
In [89]:
# Create binary thresholded images to isolate lane line pixels
def apply_thresholds(image, show=True):
img, M = birds_eye(image, display = False)
HLS = cv2.cvtColor(img, cv2.COLOR_BGR2HLS)
#h_channel = HLS[:,:,0]
#l_channel = HLS[:,:,1]
s_channel = HLS[:,:,2]
LUV = cv2.cvtColor(img, cv2.COLOR_BGR2LUV)
l_channel = LUV[:,:,0]
u_channel = LUV[:,:,1]
v_channel = LUV[:,:,2]
#Is there a difference between full color range in LAB vs. RGB?
BGR2 = cv2.cvtColor(img, cv2.COLOR_BGR2Lab)
r_channel = BGR2[:,:,2]
#g2_channel = BGR[:,:,]
#r2_channel = BGR[:,:,]
# Threshold color channel
s_thresh_min = 170
s_thresh_max = 255
s_binary = np.zeros_like(s_channel)
s_binary[(s_channel >= s_thresh_min) & (s_channel <= s_thresh_max)] = 1
r_thresh_min = 150
r_thresh_max = 200
r_binary = np.zeros_like(r_channel)
r_binary[(r_channel >= r_thresh_min) & (r_channel <= r_thresh_max)] = 1
l_thresh_min = 220
l_thresh_max = 255
l_binary = np.zeros_like(l_channel)
l_binary[(l_channel >= l_thresh_min) & (l_channel <= l_thresh_max)] = 1
combined_binary = np.zeros_like(s_binary)
combined_binary[(l_binary == 1) | (r_binary == 1)] = 1
if show == True:
# Plotting thresholded images
f, ((ax1, ax2, ax3), (ax4,ax5, ax6)) = plt.subplots(2, 3, sharey='col', sharex='row', figsize=(10,4))
f.tight_layout()
ax1.set_title('Original Image', fontsize=16)
ax1.imshow(cv2.cvtColor(undistort(image, show=False),cv2.COLOR_BGR2RGB))
ax2.set_title('Warped Image', fontsize=16)
ax2.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB).astype('uint8'))
ax3.set_title('s binary threshold', fontsize=16)
ax3.imshow(s_binary, cmap='gray')
ax4.set_title('r binary threshold', fontsize=16)
ax4.imshow(r_binary, cmap='gray')
ax5.set_title('l binary threshold', fontsize=16)
ax5.imshow(l_binary, cmap='gray')
ax6.set_title('s+l+r thresholds', fontsize=16)
ax6.imshow(combined_binary, cmap='gray')
else:
return combined_binary
In [90]:
for image in glob.glob('test_images/test*.jpg'):
apply_thresholds(image,show=True)
In [91]:
def find_lecture(binary_warped):
#print ('binary_warped shape:', binary_warped.shape)
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]/2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
#print('window_height:',window_height)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
#print ('window:',window)
#print('leftx_current:',leftx_current, 'rightx_current',rightx_current)
#print('win_y_low',win_y_low,' win_y_high',win_y_high)
#print('win_xleft_low',win_xleft_low,' win_xleft_high',win_xleft_high)
#print('win_xright_low',win_xright_low,' win_xright_high',win_xright_high)
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
return lefty, leftx, righty, rightx
In [80]:
def fill_lane(image):
combined_binary = apply_thresholds(image, show=False)
lefty, leftx, righty, rightx = find_lecture(combined_binary)
#poly fit
left_fit = np.polyfit(lefty, leftx, 2)
#make lane lines from polyfit
left_fitx = left_fit[0]*lefty**2 + left_fit[1]*lefty + left_fit[2]
right_fit = np.polyfit(righty, rightx, 2)
right_fitx = right_fit[0]*righty**2 + right_fit[1]*righty + right_fit[2]
#fill in the pixels for the curves.
rightx_int = right_fit[0]*720**2 + right_fit[1]*720 + right_fit[2]
rightx = np.append(rightx,rightx_int)
righty = np.append(righty, 720)
rightx = np.append(rightx,right_fit[0]*0**2 + right_fit[1]*0 + right_fit[2])
righty = np.append(righty, 0)
leftx_int = left_fit[0]*720**2 + left_fit[1]*720 + left_fit[2]
leftx = np.append(leftx, leftx_int)
lefty = np.append(lefty, 720)
leftx = np.append(leftx,left_fit[0]*0**2 + left_fit[1]*0 + left_fit[2])
lefty = np.append(lefty, 0)
lsort = np.argsort(lefty)
rsort = np.argsort(righty)
lefty = lefty[lsort]
leftx = leftx[lsort]
righty = righty[rsort]
rightx = rightx[rsort]
left_fit = np.polyfit(lefty, leftx, 2)
left_fitx = left_fit[0]*lefty**2 + left_fit[1]*lefty + left_fit[2]
right_fit = np.polyfit(righty, rightx, 2)
right_fitx = right_fit[0]*righty**2 + right_fit[1]*righty + right_fit[2]
# Measure Radius of Curvature for each lane line
ym_per_pix = 30./720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meteres per pixel in x dimension
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
left_curverad = ((1 + (2*left_fit_cr[0]*np.max(lefty) + left_fit_cr[1])**2)**1.5) \
/np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*np.max(lefty) + right_fit_cr[1])**2)**1.5) \
/np.absolute(2*right_fit_cr[0])
# Calculate the position of the vehicle
center = abs(640 - ((rightx_int+leftx_int)/2))
offset = 0
img_size = (img.shape[1], img.shape[0])
src = np.float32([[490, 480],[810, 480],
[1250, 720],[40, 720]])
dst = np.float32([[0, 0], [1280, 0],
[1250, 720],[40, 720]])
Minv = cv2.getPerspectiveTransform(dst, src)
warp_zero = np.zeros_like(combined_binary).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
pts_left = np.array([np.flipud(np.transpose(np.vstack([left_fitx, lefty])))])
pts_right = np.array([np.transpose(np.vstack([right_fitx, righty]))])
pts = np.hstack((pts_left, pts_right))
cv2.polylines(color_warp, np.int_([pts]), isClosed=False, color=(0,0,255), thickness = 40)
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
newwarp = cv2.warpPerspective(color_warp, Minv, (combined_binary.shape[1], combined_binary.shape[0]))
result = cv2.addWeighted(mpimg.imread(image), 1, newwarp, 0.5, 0)
f, (ax1, ax2) = plt.subplots(1,2, figsize=(9, 6))
f.tight_layout()
ax1.imshow(cv2.cvtColor((birds_eye(image, display=False)[0]), cv2.COLOR_BGR2RGB))
ax1.set_xlim(0, 1280)
ax1.set_ylim(0, 720)
ax1.plot(left_fitx, lefty, color='green', linewidth=3)
ax1.plot(right_fitx, righty, color='green', linewidth=3)
ax1.set_title('Fit Polynomial to Lane Lines', fontsize=16)
ax1.invert_yaxis() # to visualize as we do the images
ax2.imshow(result)
ax2.set_title('Fill Lane Between Polynomials', fontsize=16)
if center < 640:
ax2.text(200, 100, 'Vehicle is {:.2f}m left of center'.format(center*3.7/700),
style='italic', color='white', fontsize=10)
else:
ax2.text(200, 100, 'Vehicle is {:.2f}m right of center'.format(center*3.7/700),
style='italic', color='white', fontsize=10)
ax2.text(200, 175, 'curvature is {}m'.format(int((left_curverad + right_curverad)/2)),
style='italic', color='white', fontsize=10)
In [81]:
for image in glob.glob('test_images/test*.jpg'):
fill_lane(image)
In [28]:
class Line:
def __init__(self):
# Was the line found in the previous frame?
self.found = False
# Remember x and y values of lanes in previous frame
self.X = None
self.Y = None
# Store recent x intercepts for averaging across frames
self.x_int = deque(maxlen=10)
self.top = deque(maxlen=10)
# Remember previous x intercept to compare against current one
self.lastx_int = None
self.last_top = None
# Remember radius of curvature
self.radius = None
# Store recent polynomial coefficients for averaging across frames
self.fit0 = deque(maxlen=10)
self.fit1 = deque(maxlen=10)
self.fit2 = deque(maxlen=10)
self.fitx = None
self.pts = []
# Count the number of frames
self.count = 0
def found_search(self, x, y):
'''
This function is applied when the lane lines have been detected in the previous frame.
It uses a sliding window to search for lane pixels in close proximity (+/- 25 pixels in the x direction)
around the previous detected polynomial.
'''
MARGIN=25
xvals = []
yvals = []
#crappy code from lecture,.. better to do our own. Smaller margin => dont need to do recenter
if self.found == True:
i = 720
j = 630
while j >= 0:
yval = np.mean([i,j])
xval = (np.mean(self.fit0))*yval**2 + (np.mean(self.fit1))*yval + (np.mean(self.fit2))
x_idx = np.where((((xval - MARGIN) < x)&(x < (xval + MARGIN))&((y > j) & (y < i))))
x_window, y_window = x[x_idx], y[x_idx]
if np.sum(x_window) != 0:
np.append(xvals, x_window)
np.append(yvals, y_window)
i -= 90
j -= 90
if np.sum(xvals) == 0:
self.found = False # If no lane pixels were detected then perform blind search
return xvals, yvals, self.found
def blind_search(self, x, y, image):
'''
This function is applied in the first few frames and/or if the lane was not successfully detected
in the previous frame. It uses a slinding window approach to detect peaks in a histogram of the
binary thresholded image. Pixels in close proimity to the detected peaks are considered to belong
to the lane lines.
'''
xvals = []
yvals = []
if self.found == False:
i = 720
j = 630
while j >= 0:
histogram = np.sum(image[j:i,:], axis=0)
if self == Right:
peak = np.argmax(histogram[640:]) + 640
else:
peak = np.argmax(histogram[:640])
x_idx = np.where((((peak - 25) < x)&(x < (peak + 25))&((y > j) & (y < i))))
x_window, y_window = x[x_idx], y[x_idx]
if np.sum(x_window) != 0:
xvals.extend(x_window)
yvals.extend(y_window)
i -= 90
j -= 90
if np.sum(xvals) > 0:
self.found = True
else:
yvals = self.Y
xvals = self.X
return xvals, yvals, self.found
def radius_of_curvature(self, xvals, yvals):
ym_per_pix = 30./720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meteres per pixel in x dimension
fit_cr = np.polyfit(yvals*ym_per_pix, xvals*xm_per_pix, 2)
curverad = ((1 + (2*fit_cr[0]*np.max(yvals) + fit_cr[1])**2)**1.5) \
/np.absolute(2*fit_cr[0])
return curverad
def sort_vals(self, xvals, yvals):
sorted_index = np.argsort(yvals)
sorted_yvals = yvals[sorted_index]
sorted_xvals = xvals[sorted_index]
return sorted_xvals, sorted_yvals
def get_intercepts(self, polynomial):
bottom = polynomial[0]*720**2 + polynomial[1]*720 + polynomial[2]
top = polynomial[0]*0**2 + polynomial[1]*0 + polynomial[2]
return bottom, top
In [29]:
# Video Processing Pipeline
def process_vid(image):
img_size = (image.shape[1], image.shape[0])
# Calibrate camera and undistort image
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)
undist = cv2.undistort(image, mtx, dist, None, mtx)
# Perform perspective transform
offset = 0
src = np.float32([[490, 480],[810, 480],[1250, 720],[0, 720]])
dst = np.float32([[0, 0], [1280, 0], [1250, 720],[40, 720]])
M = cv2.getPerspectiveTransform(src, dst)
warped = cv2.warpPerspective(undist, M, img_size)
# Generate binary thresholded images
b_channel = cv2.cvtColor(warped, cv2.COLOR_RGB2Lab)[:,:,2]
l_channel = cv2.cvtColor(warped, cv2.COLOR_RGB2LUV)[:,:,0]
# Set the upper and lower thresholds for the b channel
b_thresh =(145,200)
b_binary = np.zeros_like(b_channel)
b_binary[(b_channel >= b_thresh[0]) & (b_channel <= b_thresh[1])] = 1
# Set the upper and lower thresholds for the l channel
l_thresh_min = (215,255)
l_binary = np.zeros_like(l_channel)
l_binary[(l_channel >= l_thresh[0]) & (l_channel <= l_thresh[1])] = 1
combined_binary = np.zeros_like(b_binary)
combined_binary[(l_binary == 1) | (b_binary == 1)] = 1
# Identify all non zero pixels in the image
x, y = np.nonzero(np.transpose(combined_binary))
if Left.found == True: # Search for left lane pixels around previous polynomial
leftx, lefty, Left.found = Left.found_search(x, y)
if Right.found == True: # Search for right lane pixels around previous polynomial
rightx, righty, Right.found = Right.found_search(x, y)
if Right.found == False: # Perform blind search for right lane lines
rightx, righty, Right.found = Right.blind_search(x, y, combined_binary)
if Left.found == False:# Perform blind search for left lane lines
leftx, lefty, Left.found = Left.blind_search(x, y, combined_binary)
lefty = np.array(lefty).astype(np.float32)
leftx = np.array(leftx).astype(np.float32)
righty = np.array(righty).astype(np.float32)
rightx = np.array(rightx).astype(np.float32)
# Calculate left polynomial fit based on detected pixels
left_fit = np.polyfit(lefty, leftx, 2)
# Calculate intercepts to extend the polynomial to the top and bottom of warped image
leftx_int, left_top = Left.get_intercepts(left_fit)
# Average intercepts across n frames
Left.x_int.append(leftx_int)
Left.top.append(left_top)
leftx_int = np.mean(Left.x_int)
left_top = np.mean(Left.top)
Left.lastx_int = leftx_int
Left.last_top = left_top
# Add averaged intercepts to current x and y vals
leftx = np.append(leftx, leftx_int)
lefty = np.append(lefty, 720)
leftx = np.append(leftx, left_top)
lefty = np.append(lefty, 0)
# Sort detected pixels based on the yvals
leftx, lefty = Left.sort_vals(leftx, lefty)
Left.X = leftx
Left.Y = lefty
# Recalculate polynomial with intercepts and average across n frames
left_fit = np.polyfit(lefty, leftx, 2)
Left.fit0.append(left_fit[0])
Left.fit1.append(left_fit[1])
Left.fit2.append(left_fit[2])
left_fit = [np.mean(Left.fit0),
np.mean(Left.fit1),
np.mean(Left.fit2)]
# Fit polynomial to detected pixels
left_fitx = left_fit[0]*lefty**2 + left_fit[1]*lefty + left_fit[2]
Left.fitx = left_fitx
# Calculate right polynomial fit based on detected pixels
right_fit = np.polyfit(righty, rightx, 2)
# Calculate intercepts to extend the polynomial to the top and bottom of warped image
rightx_int, right_top = Right.get_intercepts(right_fit)
# Average intercepts across 5 frames
Right.x_int.append(rightx_int)
rightx_int = np.mean(Right.x_int)
Right.top.append(right_top)
right_top = np.mean(Right.top)
Right.lastx_int = rightx_int
Right.last_top = right_top
rightx = np.append(rightx, rightx_int)
righty = np.append(righty, 720)
rightx = np.append(rightx, right_top)
righty = np.append(righty, 0)
# Sort right lane pixels
rightx, righty = Right.sort_vals(rightx, righty)
Right.X = rightx
Right.Y = righty
# Recalculate polynomial with intercepts and average across n frames
right_fit = np.polyfit(righty, rightx, 2)
Right.fit0.append(right_fit[0])
Right.fit1.append(right_fit[1])
Right.fit2.append(right_fit[2])
right_fit = [np.mean(Right.fit0), np.mean(Right.fit1), np.mean(Right.fit2)]
# Fit polynomial to detected pixels
right_fitx = right_fit[0]*righty**2 + right_fit[1]*righty + right_fit[2]
Right.fitx = right_fitx
# Compute radius of curvature for each lane in meters
left_curverad = Left.radius_of_curvature(leftx, lefty)
right_curverad = Right.radius_of_curvature(rightx, righty)
# Only print the radius of curvature every 3 frames for improved readability
if Left.count % 3 == 0:
Left.radius = left_curverad
Right.radius = right_curverad
# Calculate the vehicle position relative to the center of the lane
position = (rightx_int+leftx_int)/2
distance_from_center = abs((640 - position)*3.7/700)
Minv = cv2.getPerspectiveTransform(dst, src)
warp_zero = np.zeros_like(combined_binary).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
pts_left = np.array([np.flipud(np.transpose(np.vstack([Left.fitx, Left.Y])))])
pts_right = np.array([np.transpose(np.vstack([right_fitx, Right.Y]))])
pts = np.hstack((pts_left, pts_right))
cv2.polylines(color_warp, np.int_([pts]), isClosed=False, color=(0,0,255), thickness = 40)
cv2.fillPoly(color_warp, np.int_(pts), (34,255,34))
newwarp = cv2.warpPerspective(color_warp, Minv, (image.shape[1], image.shape[0]))
result = cv2.addWeighted(undist, 1, newwarp, 0.5, 0)
# Print distance from center on video
if position > 640:
cv2.putText(result, 'Vehicle is {:.2f}m left of center'.format(distance_from_center), (100,80),
fontFace = 16, fontScale = 2, color=(255,255,255), thickness = 2)
else:
cv2.putText(result, 'Vehicle is {:.2f}m right of center'.format(distance_from_center), (100,80),
fontFace = 16, fontScale = 2, color=(255,255,255), thickness = 2)
# Print radius of curvature on video
cv2.putText(result, 'Radius of Curvature {}(m)'.format(int((Left.radius+Right.radius)/2)), (120,140),
fontFace = 16, fontScale = 2, color=(255,255,255), thickness = 2)
Left.count += 1
return result
In [30]:
Left = Line()
Right = Line()
video_output = 'result.mp4'
clip1 = VideoFileClip("project_video.mp4").subclip(0,2)
white_clip = clip1.fl_image(process_vid)
white_clip.write_videofile(video_output, audio=False)
In [31]:
from IPython.display import HTML
HTML("""
<video width="640" height="360" controls>
<source src="{0}">
</video>
""".format('result.mp4'))
Out[31]:
In [32]:
Left = Line()
Right = Line()
challenge_output = 'challenge_result.mp4'
clip1 = VideoFileClip("challenge_video.mp4")
challenge_clip = clip1.fl_image(process_vid)
challenge_clip.write_videofile(challenge_output, audio=False)
In [ ]:
HTML("""
<video width="640" height="360" controls>
<source src="{0}">
</video>
""".format('challenge_result.mp4'))